Article
Modeling and solving a Crew Assignment Problem in air transportation
Université Paris6, LIP6, 4 Place Jussieu, 75005 Paris, France; Ecole Polytechnique de Tunisie, CORGROI, BP 743, 2078, La Marsa, Tunisia; Ecole Nationale d’Ingénieurs de Tunis, Department of Industrial Engineering, BP 37, Le Belvédère, 1002 Tunis, Tunisia
European Journal of Operational Research 01/2006; DOI: 10.1016/j.ejor.2004.11.028 Source: DBLP

Conference Paper: Handling Rest Requirements and Preassigned Activities in Airline Crew Pairing Optimization.
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ABSTRACT: For the complex task of scheduling airline crews, this paper discusses the integration of rostering requirements into the crew pairing optimization process. Our approach is based on a network flow model which uses a state expanded network to represent pairing chains for crew members at different domiciles. We enhance this model by proposing a refined representation of rest requirements along with preassignments such as pairings from the previous planning period, office and simulator activities as well as vacation and parttime leaves. In particular, we introduce the concept of availability blocks to mitigate the loss of information following from the aggregated anonymous flow of crew members in the network model. Experimental results with real world data sets show that the refined model remains tractable in practical settings.Network Optimization  5th International Conference, INOC 2011, Hamburg, Germany, June 1316, 2011. Proceedings; 01/2011 
Article: Integrated airline crew scheduling: A bidynamic constraint aggregation method using neighborhoods.
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ABSTRACT: The integrated crew scheduling (ICS) problem consists of determining, for a set of available crew members, leastcost schedules that cover all flights and respect various safety and collective agreement rules. A schedule is a sequence of pairings interspersed by rest periods that may contain days off. A pairing is a sequence of flights, connections, and rests starting and ending at the same crew base. Given its high complexity, the ICS problem has been traditionally tackled using a sequential twostage approach, where a crew pairing problem is solved in the first stage and a crew assignment problem in the second stage. Recently, Saddoune et al. (2010b) developed a model and a column generation/dynamic constraint aggregation method for solving the ICS problem in one stage. Their computational results showed that the integrated approach can yield significant savings in total cost and number of schedules, but requires much higher computational times than the sequential approach. In this paper, we enhance this method to obtain lower computational times. In fact, we develop a bidynamic constraint aggregation method that exploits a neighborhood structure when generating columns (schedules) in the column generation method. On a set of seven instances derived from realworld flight schedules, this method allows to reduce the computational times by an average factor of 2.3, while improving the quality of the computed solutions.European Journal of Operational Research. 01/2011; 212:445454.  [Show abstract] [Hide abstract]
ABSTRACT: As the service industries grow, tasks are not directly assigned to the skills but the knowledge of the worker which is to be valued more in finding the best match. The problem becomes difficult mainly because the match has to be seen with the objectives of both sides. Assignment methods fail to respond to a multiobjective, multiconstraint problem with complicated match; whereas, metaheuristics is preferable based on computational simplicity. A conditional genetic algorithm is developed in this study to propose both global and composite match using different fitness functions. This algorithm kills the infeasibilities to achieve the maximum number of matches. The proposed algorithm is applied on an academic problem of multialternative candidates and multialternative tasks (m×n problem) in two stages. In the first stage, four different fitness functions are evaluated and in the second stage using one of the fitness functions global and composite matching have been compared. The achievements will contribute both to the academic and business world.Applied Mathematical Modelling 10/2010; 34(10):27492762. · 2.16 Impact Factor
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